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US&quot;,&quot;id&quot;:&quot;region:us&quot;}],&quot;transformersInfo&quot;:{&quot;auto_model&quot;:&quot;AutoModel&quot;},&quot;hasBlockedOids&quot;:false,&quot;region&quot;:&quot;us&quot;,&quot;isQuantized&quot;:false,&quot;inferenceStatic&quot;:&quot;pipeline-not-detected&quot;,&quot;xetEnabled&quot;:false},&quot;discussionsStats&quot;:{&quot;closed&quot;:3,&quot;open&quot;:5,&quot;total&quot;:8},&quot;query&quot;:{},&quot;inferenceProviders&quot;:[{&quot;name&quot;:&quot;hyperbolic&quot;,&quot;enabled&quot;:true,&quot;position&quot;:0,&quot;isReleased&quot;:true,&quot;accuratePricing&quot;:false},{&quot;name&quot;:&quot;cerebras&quot;,&quot;enabled&quot;:true,&quot;position&quot;:1,&quot;isReleased&quot;:true,&quot;accuratePricing&quot;:false},{&quot;name&quot;:&quot;sambanova&quot;,&quot;enabled&quot;:true,&quot;position&quot;:2,&quot;isReleased&quot;:true,&quot;accuratePricing&quot;:false},{&quot;name&quot;:&quot;novita&quot;,&quot;enabled&quot;:true,&quot;position&quot;:3,&quot;isReleased&quot;:true,&quot;accuratePricing&quot;:true},{&quot;name&quot;:&quot;fireworks-ai&quot;,&quot;enabled&quot;:true,&quot;position&quot;:4,&quot;isReleased&quot;:true,&quot;accuratePricing&quot;:false},{&quot;name&quot;:&quot;fal-ai&quot;,&quot;enabled&quot;:true,&quot;position&quot;:5,&quot;isReleased&quot;:true,&quot;accuratePricing&quot;:true},{&quot;name&quot;:&quot;replicate&quot;,&quot;enabled&quot;:true,&quot;position&quot;:6,&quot;isReleased&quot;:true,&quot;accuratePricing&quot;:false},{&quot;name&quot;:&quot;nebius&quot;,&quot;enabled&quot;:true,&quot;position&quot;:7,&quot;isReleased&quot;:true,&quot;accuratePricing&quot;:false},{&quot;name&quot;:&quot;together&quot;,&quot;enabled&quot;:true,&quot;position&quot;:8,&quot;isReleased&quot;:true,&quot;accuratePricing&quot;:false},{&quot;name&quot;:&quot;hf-inference&quot;,&quot;enabled&quot;:true,&quot;position&quot;:9,&quot;isReleased&quot;:true,&quot;accuratePricing&quot;:true}]}"><header 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	<span>Transformers</span>
	

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	<span>Safetensors</span>
	

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	<span>doi:10.57967/hf/3036</span>
	

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		<span>Inference Endpoints</span>
	

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	<span class="-mr-2 text-gray-400">arxiv:</span>
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	<span class="-mr-2 text-gray-400">arxiv:</span>
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	<!-- HTML_TAG_START --><h1 class="relative group flex items-center">
	<a rel="nofollow" href="#docling-models" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" id="docling-models">
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	</a>
	<span>
		Docling Models
	</span>
</h1>
<p>This page contains models that power the PDF document converion package <a rel="nofollow" href="https://github.com/DS4SD/docling">docling</a>.</p>
<h2 class="relative group flex items-center">
	<a rel="nofollow" href="#layout-model" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" id="layout-model">
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	</a>
	<span>
		Layout Model
	</span>
</h2>
<p>The layout model will take an image from a poge and apply RT-DETR model in order to find different layout components. It currently detects the labels: Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title. As a reference (from the DocLayNet-paper), this is the performance of standard object detection methods on the DocLayNet dataset compared to human evaluation, </p>
<div class="max-w-full overflow-auto">
	<table>
		<thead><tr>
<th></th>
<th>human</th>
<th>MRCNN</th>
<th>MRCNN</th>
<th>FRCNN</th>
<th>YOLO</th>
</tr>

		</thead><tbody><tr>
<td></td>
<td>human</td>
<td>R50</td>
<td>R101</td>
<td>R101</td>
<td>v5x6</td>
</tr>
<tr>
<td>Caption</td>
<td>84-89</td>
<td>68.4</td>
<td>71.5</td>
<td>70.1</td>
<td>77.7</td>
</tr>
<tr>
<td>Footnote</td>
<td>83-91</td>
<td>70.9</td>
<td>71.8</td>
<td>73.7</td>
<td>77.2</td>
</tr>
<tr>
<td>Formula</td>
<td>83-85</td>
<td>60.1</td>
<td>63.4</td>
<td>63.5</td>
<td>66.2</td>
</tr>
<tr>
<td>List-item</td>
<td>87-88</td>
<td>81.2</td>
<td>80.8</td>
<td>81.0</td>
<td>86.2</td>
</tr>
<tr>
<td>Page-footer</td>
<td>93-94</td>
<td>61.6</td>
<td>59.3</td>
<td>58.9</td>
<td>61.1</td>
</tr>
<tr>
<td>Page-header</td>
<td>85-89</td>
<td>71.9</td>
<td>70.0</td>
<td>72.0</td>
<td>67.9</td>
</tr>
<tr>
<td>Picture</td>
<td>69-71</td>
<td>71.7</td>
<td>72.7</td>
<td>72.0</td>
<td>77.1</td>
</tr>
<tr>
<td>Section-header</td>
<td>83-84</td>
<td>67.6</td>
<td>69.3</td>
<td>68.4</td>
<td>74.6</td>
</tr>
<tr>
<td>Table</td>
<td>77-81</td>
<td>82.2</td>
<td>82.9</td>
<td>82.2</td>
<td>86.3</td>
</tr>
<tr>
<td>Text</td>
<td>84-86</td>
<td>84.6</td>
<td>85.8</td>
<td>85.4</td>
<td>88.1</td>
</tr>
<tr>
<td>Title</td>
<td>60-72</td>
<td>76.7</td>
<td>80.4</td>
<td>79.9</td>
<td>82.7</td>
</tr>
<tr>
<td>All</td>
<td>82-83</td>
<td>72.4</td>
<td>73.5</td>
<td>73.4</td>
<td>76.8</td>
</tr>
</tbody>
	</table>
</div>
<h2 class="relative group flex items-center">
	<a rel="nofollow" href="#tableformer" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" id="tableformer">
		<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black dark:hover:text-gray-200 w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
	</a>
	<span>
		TableFormer
	</span>
</h2>
<p>The tableformer model will identify the structure of the table, starting from an image of a table. It uses the predicted table regions of the layout model to identify the tables. Tableformer has SOTA table structure identification,</p>
<div class="max-w-full overflow-auto">
	<table>
		<thead><tr>
<th>Model (TEDS)</th>
<th>Simple table</th>
<th>Complex table</th>
<th>All tables</th>
</tr>

		</thead><tbody><tr>
<td>Tabula</td>
<td>78.0</td>
<td>57.8</td>
<td>67.9</td>
</tr>
<tr>
<td>Traprange</td>
<td>60.8</td>
<td>49.9</td>
<td>55.4</td>
</tr>
<tr>
<td>Camelot</td>
<td>80.0</td>
<td>66.0</td>
<td>73.0</td>
</tr>
<tr>
<td>Acrobat Pro</td>
<td>68.9</td>
<td>61.8</td>
<td>65.3</td>
</tr>
<tr>
<td>EDD</td>
<td>91.2</td>
<td>85.4</td>
<td>88.3</td>
</tr>
<tr>
<td>TableFormer</td>
<td>95.4</td>
<td>90.1</td>
<td>93.6</td>
</tr>
</tbody>
	</table>
</div>
<h2 class="relative group flex items-center">
	<a rel="nofollow" href="#references" class="block pr-1.5 text-lg md:absolute md:p-1.5 md:opacity-0 md:group-hover:opacity-100 md:right-full" id="references">
		<span class="header-link"><svg viewBox="0 0 256 256" preserveAspectRatio="xMidYMid meet" height="1em" width="1em" role="img" aria-hidden="true" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" class="text-gray-500 hover:text-black dark:hover:text-gray-200 w-4"><path fill="currentColor" d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z"></path></svg></span>
	</a>
	<span>
		References
	</span>
</h2>
<pre><code>@techreport{Docling,
  author = {Deep Search Team},
  month = {8},
  title = {{Docling Technical Report}},
  url={https://arxiv.org/abs/2408.09869},
  eprint={2408.09869},
  doi = "10.48550/arXiv.2408.09869",
  version = {1.0.0},
  year = {2024}
}

@article{doclaynet2022,
  title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},  
  doi = {10.1145/3534678.353904},
  url = {https://arxiv.org/abs/2206.01062},
  author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
  year = {2022}
}

@InProceedings{TableFormer2022,
    author    = {Nassar, Ahmed and Livathinos, Nikolaos and Lysak, Maksym and Staar, Peter},
    title     = {TableFormer: Table Structure Understanding With Transformers},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {4614-4623},
    doi = {https://doi.org/10.1109/CVPR52688.2022.00457}
}
</code></pre>
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